Optimal Adaptive Control of Fed-Batch Fermentation Processes

Abstract

This paper presents a unifying methodology for optimization of biotechnological processes, namely optimal adaptive control, which combines the advantages of both the optimal control and the adaptive control approaches. As an example, the design of a substrate feeding rate controller for a class of biotechnologlcal processes in stirred tank reactors characterized by a decoupling between biomass growth and product formation is considered. More specifically, the most common case is considered of a process with monotonic specific growth rate and non-monotonic specific production rate as functions of substrate concentration. The main contribution is to illustrate how the insight, obtained by preliminary optimal control studies, leads to the design of easy-to-implement adaptive controllers. The controllers derived in this way combine a nearly optimal performance with good robustness properties against modeling uncertainties and process disturbances. Since they can be considered model-independent, they may be very helpful also in solving the model discrimination problem, which often occurs during biotechnological process modeling. To illustrate the method and the results obtained, simulation results are given for the penicillin G fed-batch fermentation process.

Efficient deterministic multiple objective optimal control of (bio)chemical processes

“…These types of arc sequences have been reported by, e.g., Van Impe et al (1994), Van Impe (1998) and Van Impe and Bastin (1998), as generic switching structures for various fermentation processes and cost functions. When only productivity is aimed at (i.e., w = 0), the initial max part is present in order to stimulate biomass growth, and hence lysine production.…”

Quantitative Analysis of Bacterial Gene Expression by Using the gusA Reporter Gene System

“…Therefore, only the specific rate of expression of the fusion protein, which is independent of the experimental design, can reflect the influence of O 2 on expression of the target gene. In order to derive the specific rate of expression of the fusion protein from the ␤-glucuronidase activity measured, the following general dynamic mathematical model based on mass balances was applied (28): …”

Time-varying system identification using modulating functions and spline models with application to bio-processes

“…While any such interpretation is limited by the validity of the model, changes in model parameters may indicate physico chemical changes inside the process (Holmberg & Ranta, 1982). The rational design of advanced control and optimization schemes for bio-processes is therefore intricately linked to the identification of kinetic rate parameters accurately and in real-time (Van Impe & Bastin, 1995;Thatipamala, Hill & Rohani, 1996).…”